The multi-robot coordination task is investigated in its Representation Space (RS). First, the RS model of the robot system as well as its prescribed task is formulated by clarifying the internal and external constraints affecting task realization. All constraints are depicted as unreachable areas in RS model. Then, whether a task is feasible or not is transformed to check 1) if the final repre...

A mobile robot has always uncertainty about the world model. Reducing this uncertainty is very hard because there is a huge amount of information and the robot must focus on the most relevant one. The selection of the most relevant information must be based on the task the robot is executing, but there could be several sources of information where the robot would like to focus on. This is also ...

This research presents a dynamic level control algorithm to meet simultaneously multiple desired tasks based on their allocated priorities for redundant robotic systems. It is shown that this algorithm can be treated as a general framework to achieve control over the whole body of the robot. The control law is an extension of the well-known acceleration based control to redundant robots, and co...

This paper describes a methodology for automatically synthesizing task solutions for heterogeneous multi-robot teams. In contrast to prior approaches that require a manual predefinition of how the robot team will accomplish its task (while perhaps automating who performs which task), our approach automates both the how and the who to generate task solution approaches that were not explicitly de...

Currently, most robot programming is done either by manual programming or by the "teach-by-showing" method using a teach pendant. Both of these methods have been found to have several drawbacks. We propose a novel method to program a robot, the assembly-plan-fromobservation (APO) method. The APO method aims to build a system that has the capability of observing a human performing an assembly ta...

We are exploring the use of auction mechanisms to assign roles within a team of agents operating in a dynamic environment. Depending on the degree of collaboration between the agents and the specific auction policies employed, we can obtain varying combinations of role assignments that can affect both the speed and the quality of task execution. In order to examine this extremely large set of c...

Large scale operational areas often require multiple service robots for coverage and task parallelism. In such scenarios, each robot keeps its individual map of the environment and serves specific areas of the map at different times. We propose a knowledge sharing mechanism for multiple robots in which one robot can inform other robots about the changes in map, like path blockage, or new static...

As we progress towards a world where robots play an integral role in society, a critical problem that remains to be solved is the Pickup Team Challenge; that is, dynamically formed heterogeneous robot teams executing coordinated tasks where little information is known a priori about the tasks, the robots, and the environments in which they will operate. Successful solutions to forming pickup te...

Conventional robots find their limitation if the task execution requires a level of perception, dexterity and decision making which cannot be met technically in a costeffective or a robust way. However, within the otherwise manual task execution less demanding subtasks may still be carried out automatically. A safe and flexible co-operation between robot and operator may be a promising way to a...

Task allocation is an issue that every multi-robot system must address. Recent task allocation solutions propose an auction based approach wherein robots bid for tasks based on cost functions for performing a task. This paper presents RACHNA, a novel architecture for multi-robot task allocation based on a modified algorithm for the winner determination problem in multi-unit combinatorial auctio...